Hi,
Pairwise similarity is a quadratic problem and its very easy to run into
a problem size does not scale anymore, especially with so many items.
Our code downsamples the input data to help with this.
One thing you can do is decrease the argument maxNumInteractions to a
lower number to increase the amount of downsampling. Another thing you
can do is to remove the items with the highest amount of interactions
from the dataset as they are not very interesting usually (everybody
knows the topsellers already) and heavily impact the computation.
Best,
Sebastian
On 23.06.2016 15:47, jelmer wrote:
Hi,
I am trying to build a simple recommendation engine using spark item
similarity (eg with
org.apache.mahout.math.cf.SimilarityAnalysis.cooccurrencesIDSs)
Things work fine on comparatively small dataset but I am having difficulty
scaling it up
The input I am using is CSV data containing 19.988.422 view item events
produced by 1.384.107 users. Looking at 5.135.845 distinct products
The csv data is stored on hdfs and is split up over 15 files, consequently
the resultant RDD will have 15 partitions.
After tweaking some parameters I did manage to get the job to run without
going out of memory but the job takes a very very long time to run
After running for 15 hours it still is stuck on
org.apache.spark.rdd.RDD.flatMap(RDD.scala:332)
org.apache.mahout.sparkbindings.blas.AtA$.at_a_nongraph_mmul(AtA.scala:254)
org.apache.mahout.sparkbindings.blas.AtA$.at_a(AtA.scala:61)
org.apache.mahout.sparkbindings.SparkEngine$.tr2phys(SparkEngine.scala:325)
org.apache.mahout.sparkbindings.SparkEngine$.tr2phys(SparkEngine.scala:339)
org.apache.mahout.sparkbindings.SparkEngine$.toPhysical(SparkEngine.scala:123)
org.apache.mahout.math.drm.logical.CheckpointAction.checkpoint(CheckpointAction.scala:41)
org.apache.mahout.math.drm.package$.drm2Checkpointed(package.scala:95)
org.apache.mahout.math.cf.SimilarityAnalysis$$anonfun$3.apply(SimilarityAnalysis.scala:145)
org.apache.mahout.math.cf.SimilarityAnalysis$$anonfun$3.apply(SimilarityAnalysis.scala:143)
scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
scala.collection.Iterator$class.foreach(Iterator.scala:727)
scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
scala.collection.mutable.ListBuffer.$plus$plus$eq(ListBuffer.scala:176)
scala.collection.mutable.ListBuffer.$plus$plus$eq(ListBuffer.scala:45)
scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)scala.collection.AbstractIterator.to(Iterator.scala:1157)
scala.collection.TraversableOnce$class.toList(TraversableOnce.scala:257)
scala.collection.AbstractIterator.toList(Iterator.scala:1157)
I am using spark on yarn and containers cannot use more than 16gb
I figured I would be able to speed things up by throwing a larger number of
executors at the problem. but so far that is not working out very well
I tried assigning 500 executors and repartitioning the input data to 500
partitions and even changing the spark.yarn.driver.memoryOverhead to crazy
values (half of the heap) did not resolve this.
Could someone offer any guidance on how to best speed up item similarity
jobs ?
